Analysis of SNPs

Setup

library(SeqArray)
library(SNPRelate)
library(pander)
library(scales)
library(magrittr)
library(tidyverse)
library(readxl)
library(sp)
library(ggmap)
library(rgdal)
library(ggsn)
library(parallel)
library(qqman)
library(ggrepel)
library(plyranges)
library(rtracklayer)

theme_set(theme_bw())
panderOptions("missing", "")
mc <- min(12, detectCores() - 1)
alpha <- 0.05

w <- 169/25.4
h <- 169/25.4

This workflow immediately follows 03_SNPFiltering and performs an analysis on the SNPs retained after filtering. Analysis performed is:

• Assess the rate of missing values by retained SNP and sample
• Run a PCA analysis using SNPs & samples to minimise missing values
• Execute the previous workflow

gdsPath <- file.path("..", "5_stacks", "gds", "populations.snps.gds")
gdsFile <- seqOpen(gdsPath, readonly = TRUE)

keepSNPs <- readRDS("keepSNPsAfterLDPruning.RDS")
sampleID <- tibble(
    Sample = seqGetData(gdsFile, "sample.annotation/Sample"),
    Population = seqGetData(gdsFile, "sample.annotation/Population"),
    Location = seqGetData(gdsFile, "sample.annotation/Location")
)
popSizes <- sampleID %>% 
    group_by(Population) %>%
    summarise(n = dplyr::n())

snps <- tibble(
    variant.id = seqGetData(gdsFile, "variant.id") ,
    chromosome = seqGetData(gdsFile, "chromosome"),
    position = seqGetData(gdsFile, "position"),
    snpID = seqGetData(gdsFile, "annotation/id")
) %>%
    mutate(snpID = str_remove(snpID, ":.$"),
           snpID = str_replace(snpID, ":", "_")) %>%
    separate(snpID, into = c("Locus ID", "Col"), remove = FALSE) %>%
    mutate_at(vars(`Locus ID`, Col), as.integer) %>%
    mutate(Col = Col - 1,
           snpID = paste(`Locus ID`, Col, sep = "_")) %>%
    right_join(keepSNPs)
seqSetFilter(gdsFile, variant.id = snps$variant.id)

## # of selected variants: 18,886

genotypes <- snps %>%
    cbind(seqGetData(gdsFile, "genotype") %>% 
              apply(MARGIN = 3, colSums) %>%
              t %>%
              set_colnames(sampleID$Sample)) %>%
    as_tibble() %>%
    gather(Sample, Genotype, one_of(sampleID$Sample)) %>%
    dplyr::filter(!is.na(Genotype)) %>%
    arrange(variant.id, Sample) %>%
    left_join(sampleID)
seqResetFilter(gdsFile)

## # of selected samples: 146
## # of selected variants: 146,763

• After loading the data from the previous section, an additional fitering step was performed, where SNPs where required have a non-zero MAF in the initial 1996 population.

snpIn1996 <- genotypes %>% 
    filter(Population == 1996) %>% 
    group_by(variant.id) %>% 
    summarise(maf = mean(Genotype) / 2) %>%
    filter(maf > 0)
genotypes %<>% 
    right_join(snpIn1996)

• This additional filtering step restricted analysis to 18,878 SNPs instead of the initial 18,886 SNPs

seqClose(gdsFile)

In addition to the above, the set of genes corresponding the Ensembl Release 84 were loaded.

ensGenes <- file.path("..", "external", "Oryctolagus_cuniculus.OryCun2.0.96.gff3.gz") %>%
    import.gff3(feature.type = "gene", sequenceRegionsAsSeqinfo = TRUE) %>%
    .[,c("gene_id", "Name", "description")]

The set of SNPs under invetigation was also defined as a GRanges object.

snpsGR <- makeGRangesFromDataFrame(
    df = snps, 
    keep.extra.columns = TRUE, 
    ignore.strand = TRUE, 
    seqinfo = seqinfo(ensGenes), 
    seqnames.field = "chromosome", 
    start.field = "position", 
    end.field = "position"
)

This was intersected with the set of genes to connect SNPs to genes within 40kb.

snp2Gene <- snpsGR %>% 
    resize(width = 199999) %>%
    trim() %>%
    join_overlap_inner(ensGenes) 

PCA

lowCall <- c("gc2901", "gc2776", "gc2731", "gc2727", "gc2686")

snp4PCA <- genotypes %>% 
    filter(!Sample %in% lowCall) %>%
    group_by(variant.id, Population) %>% 
    summarise(n = dplyr::n()) %>% 
    spread(Population, n) %>%
    mutate(N = `1996` + `2010` + `2012`) %>% 
    ungroup() %>%
    filter(N > 0.95*(sum(popSizes$n) - length(lowCall)))
pca <- genotypes %>%
    filter(variant.id %in% snp4PCA$variant.id) %>%
    dplyr::select(variant.id, Sample, Genotype) %>%
    spread(Sample, Genotype) %>%
    as.data.frame() %>%
    column_to_rownames("variant.id") %>%
    as.matrix() %>%
    apply(2, function(x){
        x[is.na(x)] <- mean(x, na.rm = TRUE)
        x
    }) %>%
    t() %>%
    .[, apply(., 2, function(x){length(unique(x)) > 1})] %>%
    prcomp( center = TRUE)

As noted in the previous section sample samples gc2901, gc2776, gc2731, gc2727 and gc2686 had a SNP identification rate \(< 50\)% and as such these were markd as potential outliers. Ignoring these samples, and restricting data to SNPs identified in \(>95\)% of all samples, a preliminary PCA was performed This amounted to 7,767 of the possible 18,878 SNPs for analysis using PCA. Missing values were specified as the mean MAF over all populations combined.

Given the initially observed structure, in which samples from the 2012 population are separating from the other samples which group with the 1996 population, the collection points for the 2012 samples as investigated.

sampleID %<>%
    left_join(
        file.path("..", "external", "GPS_Locations.xlsx") %>%
            read_excel() %>%
            dplyr::select(Sample, ends_with("tude")) %>%
            mutate(Sample = gsub("[Oo][Rr][Aa] ([0-9ABC]*)", "ora\\1", Sample))
    )

PCA showing population structure. Point size reflects the proportion of SNPs for which imputation was required, and the observed structure appeared independent of this.

loc <- c(range(sampleID$Longitude, na.rm = TRUE) %>% mean,
         range(sampleID$Latitude, na.rm = TRUE) %>% mean)
saPoly <- readRDS(file.path("..", "external", "saPoly.RDS"))
roads <- readRDS(file.path("..", "external", "roads.RDS"))
gc <- SpatialPoints(cbind(x = 138.655972, y = -31.200305))
proj4string(gc) <- "+proj=longlat +ellps=GRS80 +no_defs"
xBreaks <- seq(138.65, 138.8, by = 0.05)
xLabs <- parse(text = paste(xBreaks, "*degree ~ E", sep = ""))
yBreaks <- seq(-31.2, -31.32, by = -0.04)
yLabs <- parse(text = paste(-yBreaks, "*degree ~ S", sep = ""))
leftN <- tibble(x = c(138.7965, 138.8, 138.8) - 0.01,
                    y = c(-31.2, -31.198, -31.193))
rightN <- tibble(x = c(138.8, 138.8, 138.8035) - 0.01,
                     y = c(-31.193, -31.198, -31.2))

ggMap <- get_map(loc, zoom = 12, maptype = "terrain", color = "bw")
zoomPlot <- ggmap(ggMap, extent = "normal", maprange = FALSE) +
    geom_path(
        aes(long, lat, group = group), 
        data = subset(roads, SURFACE == "UNSE"), 
        linetype = 2, size = 0.3) + 
    geom_path(
        aes(long, lat, group = group), 
        data = subset(roads, SURFACE != "UNSE"), 
        linetype = 1, size = 0.4) +
    geom_label(x = 138.74, y = -31.29, label = "Flinders Ranges NP", alpha = 0.4) +
    geom_label(x = 138.72, y = -31.22, label = "Gum Creek", alpha = 0.4) +
    geom_point(aes(x, y), data = as.data.frame(gc), shape = 3, size = 3) +
    geom_text(aes(x, y), label = "HS", data = as.data.frame(gc), nudge_y = 0.005) +
    geom_polygon(
        data = subset(saPoly, F_CODE == "HD"),
        aes(long, lat, group = group),
        fill = rgb(1, 1, 1, 0), colour = "grey10", size = 0.3) +
    geom_polygon(
        data = subset(saPoly, F_CODE == "PARK"),
        aes(long, lat, group = group),
        fill = rgb(1, 1, 1, 0), colour = "grey10", size = 0.2) +
    geom_point(
        data= filter(pca4Plot, grepl("2012", Population)),
        aes(Longitude, Latitude, colour = Population),
        size = 0.9*ps) +
    geom_polygon(aes(x, y), data = leftN, fill = "white", colour = "grey10", size = 0.4) +
    geom_polygon(aes(x, y), data = rightN, fill = "grey10", colour = "grey10", size = 0.4) +
    geom_text(x = 138.79, y = -31.19, label = "N", 
              colour = "grey10", size = 4) +
    scale_colour_manual(values = popCols[2:3]) +
    coord_cartesian(xlim = c(138.618, 138.81),
                    ylim = c(-31.335, -31.18),
                    expand = 0) +
    scale_x_continuous(breaks = xBreaks, labels = xLabs) +
    scale_y_continuous(breaks = yBreaks, labels = yLabs) +
    guides(colour = FALSE) +
    ggsn::scalebar(x.min = 138.618, x.max = 138.81,
                   y.min = -31.335, y.max = -31.18,
                   transform = TRUE,
                   dist = 2, dist_unit = "km",
                   model = 'GRS80',
                   height = 0.012, st.size = 4,
                   location = 'bottomright',
                   anchor = c(x = 138.8, y = -31.328)) +
    labs(x = "Longitude",
         y = "Latitude") +
    theme(text = element_text(size = fs),
          plot.margin = unit(c(1, 1, 1, 1), "mm"))

ausPolygon <- readRDS(file.path("..", "external", "ausPolygon.RDS"))
ausPts <- SpatialPoints(cbind(x = loc[1], y = loc[2]))
proj4string(ausPts) <- proj4string(ausPolygon)
ausPlot <- ggplot() +
  geom_polygon(data = ausPolygon, 
               aes(long, lat, group = group), fill = "white", colour = "black") + 
  geom_point(data = as.data.frame(ausPts), aes(x, y), size = 1.5) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white", colour = "black"))

## png 
##   2

Figure 1: Collection points for all 2012 samples with colours showing sub-populations initially defined by PCA analysis and k-means clustering.

zoomLoc <- c(138.753, -31.242)
xBreaks <- seq(138.74, 138.76, by = 0.01)
xLabs <- parse(text = paste(xBreaks, "*degree ~ W", sep = ""))
yBreaks <- seq(-31.235, -31.25, by = -0.005)
yLabs <- parse(text = paste(-yBreaks, "*degree ~ S", sep = ""))
central <- rbind(x = c(138.749, 138.755),
                 y = c(-31.2365, -31.2495)) %>%
  set_colnames(c("min", "max"))
leftN <- tibble(x = c(138.7595, 138.76, 138.76),
                    y = c(-31.234, -31.2335, -31.2325))
rightN <- tibble(x = c(138.76, 138.76, 138.7605),
                     y = c(-31.2325, -31.2335, -31.234))
get_map(zoomLoc, zoom = 15, maptype = "terrain", source = "google", color = "bw") %>%
    ggmap() +
    geom_jitter(
        data = filter(pca4Plot, grepl("2012", Population)), 
        aes(Longitude, Latitude, colour = Population), 
        size = 3, width = 0.0005, height = 0) +
    geom_rect(
        xmin = central["x", "min"],
        xmax = central["x", "max"],
        ymin = central["y", "min"],
        ymax = central["y", "max"],
        fill = "red", alpha = 0.01, colour = "black") +
    geom_polygon(aes(x, y), data = leftN, fill = "white", colour = "grey10", size = 0.4) +
    geom_polygon(aes(x, y), data = rightN, fill = "grey10", colour = "grey10", size = 0.4) +
    geom_text(x = 138.76, y = -31.232, label = "N", 
              colour = "grey10", size = 5) +
    scale_colour_manual(values = popCols[2:3]) +
    scale_x_continuous(breaks = xBreaks, labels = xLabs) +
    scale_y_continuous(breaks = yBreaks, labels = yLabs) +
    theme_bw() +
    guides(colour = FALSE) +
    labs(x = "Longitude",
         y = "Latitude") +
    coord_cartesian(xlim = c(138.74, 138.762),
                    ylim = c(-31.253, -31.231),
                    expand = 0) +
    ggsn::scalebar(
        x.min = 138.74, x.max = 138.762, 
        y.min = -31.253, y.max = -31.231,
        transform = TRUE,
        dist = 0.25, dist_unit = "km",, model = 'GRS80', 
        height = 0.012, st.size = 4,
        location = 'bottomright',
        anchor = c(x = 138.761, y = -31.252)
    )

Zoomed-in view of the central region for 2012 samples with colours showing sub-populations defined by PCA analysis. The region considered to be the Central Region is shaded in red. Due to overlapping GPS points a small amount of jitter has been added to the x-axis.

Region Analysis

Removal of SNPs Associated with Collection Region

The structure observed within the 2012 population in the PCA could possibly be explained by recent migration into this region. As the samples collected in the outer regions appeared very similar to the 1996 population in the above plots, this would possibly indicate migration a very recent event as the genetic influence of this has not spread through the wider area. Although this may be due to other factors such as sampling bias, this structure was addressed by identifying SNPs which showed an association with the sub-populations identified by PCA analysis. In this way, any candidate SNPs obtained below will be less impacted by this structure, and will be more reflective of the intended variable under study, i.e. selection over time, as opposed to any internal structure of the 2012 population.

oraRegions <- pca4Plot %>% 
  filter(grepl("2012", Population)) %>% 
  rowwise() %>%
  mutate(yCentral = cut(Latitude, breaks = central["y",], include.lowest = TRUE), 
         xCentral = cut(Longitude, breaks = central["x",], include.lowest = TRUE),
         Central = (is.na(yCentral) + is.na(xCentral)) == 0) %>%
  dplyr::select(Sample, Central) 

Testing for Structure in 2012

This model tests:
H₀: No association between genotypes and collection region
H_A: An association exists between genotypes and collection region

regionResults <- genotypes %>%
    filter(Population == 2012) %>%
    split(f = .$variant.id) %>%
    mclapply(function(x){
        ft <- list(p.value = NA)
        if (length(unique(x$Genotype)) > 1) {
            ft <- x %>%
                left_join(oraRegions) %>%
                group_by(Genotype, Central) %>%
                tally() %>%
                spread(Genotype, n, fill = 0) %>%
                column_to_rownames("Central") %>%
                fisher.test()
        }
        x %>%
            distinct(variant.id, snpID, chromosome, position) %>%
            mutate(p = ft$p.value)
    }, mc.cores = mc) %>%
    bind_rows() %>%
    filter(!is.na(p)) %>%
    arrange(p)

A total of 1682 SNPs were detected as showing a significant association between genotype and the collection region. Under H₀, the number expected using α = 0.05 would be 943, and as this number was approximately double that expected, this was taken as evidence of this being a genuine point of concern for this dataset.

Notably, Type II errors were of principle concern in this instance, and as such every SNP with p < 0.05 in the above test was excluded from downstream analysis.

regionSNPs <- filter(regionResults, p < 0.05)
saveRDS(regionSNPs, "regionSNPs.RDS")

Under this additional filtering step, the original set of 18784 SNPs will be reduced to 17,102 for testing by genotype and allele frequency.

Verification Of Removal

In order to verify that the removal of the above SNPs removed the undesired population structure from the 2012 population, the above PCA was repeated, excluding the SNPs marked for removal. The previous structure noted in the data was no longer evident, and as such, these SNPs were marked for removal during analysis by genotype and allele frequency.

pcaPost <- genotypes %>%
    filter(variant.id %in% snp4PCA$variant.id,
           !variant.id %in% regionSNPs$variant.id) %>%
    dplyr::select(variant.id, Sample, Genotype) %>%
    spread(Sample, Genotype) %>%
    as.data.frame() %>%
    column_to_rownames("variant.id") %>%
    as.matrix() %>%
    apply(2, function(x){
        x[is.na(x)] <- mean(x, na.rm = TRUE)
        x
    }) %>%
    t() %>%
    .[, apply(., 2, function(x){length(unique(x)) > 1})] %>%
    prcomp( center = TRUE) 
pcaPost4Plot <- pcaPost$x %>%
    as.data.frame() %>%
    rownames_to_column("Sample") %>%
    as_tibble() %>%
    dplyr::select(Sample, PC1, PC2, PC3) %>%
    left_join(sampleID) %>%
    mutate(Cluster = kmeans(cbind(PC1, PC2, PC3), 3)$cluster) %>%
    group_by(Cluster) %>%
    mutate(maxY = max(Latitude, na.rm = TRUE)) %>%
    ungroup() %>%
    mutate(Population = case_when(
        Population == 1996 ~ "1996",
        Population == 2010 ~ "Outgroup (Turretfield)",
        maxY == max(maxY) ~ "2012 (Outer)",
        maxY != max(maxY) ~ "2012 (Central)"
    )) %>%
    left_join(genotypes %>% 
                  filter(variant.id %in% snp4PCA$variant.id,
                         !Sample %in% lowCall) %>% 
                  group_by(Sample) %>% 
                  tally() %>% 
                  mutate(imputationRate = 1 - n / nrow(snp4PCA))) 

Figure 2: Principal Components Analysis showing structures before removal of SNPs denoting collection region in the 2012 population, and after removal of these SNPs

SNP Analysis

Genotype Frequency Model

This model tests:
H₀: No association between genotypes and populations
H_A: An association exists between genotypes and populations

genotypeResults <- genotypes %>%
    filter(Population != 2010,
           !variant.id %in% regionSNPs$variant.id) %>%
    group_by(variant.id, snpID, Population, Genotype) %>%
    tally() %>%
    ungroup() %>%
    split(f = .$variant.id) %>%
    mclapply(function(x){
        ft <- list(p.value = NA)
        if (length(unique(x$Genotype)) > 1) {
            ft <- x %>%
                spread(Genotype, n, fill = 0) %>%
                column_to_rownames("Population") %>%
                dplyr::select(-variant.id, -snpID) %>%
                fisher.test()
        }
        x %>% 
            distinct(variant.id) %>%
            mutate(p = ft$p.value)
    },mc.cores = mc) %>%
    bind_rows() %>%
    filter(!is.na(p)) %>%
    mutate(FDR = p.adjust(p, "fdr"),
           adjP = p.adjust(p, "bonferroni")) %>%
    arrange(p) %>%
    left_join(genotypes %>%
                  distinct(variant.id, snpID, chromosome, position)) %>%
    dplyr::select(variant.id, snpID, chromosome, position, everything())

Under the full genotype model:

• 15 genotypes were detected as being significantly associated with the two populations when controlling the FWER at α = 0.05
• 38 genotypes were detected as being significantly associated with the two populations when controlling the FDR at α = 0.05
• For the most highly ranked SNP (3391201_92), the minor allele has been completely lost in the 2012 population

SNPs with adjusted p-values < 0.05 when analysing by genotype.

snpID	chromosome	position	p	FDR	adjP	Gene within 100kb
3391201_92	GL018705	1,937,359	6.103e-08	0.0009035	0.001047
686773_29	3	90,851,512	1.053e-07	0.0009035	0.001807	CRISPLD1
686773_29	3	90,851,512	1.053e-07	0.0009035	0.001807
916731_91	4	89,602,973	1.865e-07	0.0009837	0.003201	RIC8B
916731_91	4	89,602,973	1.865e-07	0.0009837	0.003201	RFX4
836950_151	4	35,111,855	2.587e-07	0.0009837	0.00444	TMPRSS12
836950_151	4	35,111,855	2.587e-07	0.0009837	0.00444	METTL7A
836950_151	4	35,111,855	2.587e-07	0.0009837	0.00444	SLC11A2
321186_117	2	2.8e+07	2.866e-07	0.0009837	0.004919	KLF3
2227006_109	13	129,650,867	4.419e-07	0.001051	0.007583	ID3
2227006_109	13	129,650,867	4.419e-07	0.001051	0.007583	E2F2
2227006_109	13	129,650,867	4.419e-07	0.001051	0.007583	ASAP3
2227006_109	13	129,650,867	4.419e-07	0.001051	0.007583	TCEA3
2227006_109	13	129,650,867	4.419e-07	0.001051	0.007583
3040789_114	19	30,689,426	4.838e-07	0.001051	0.008303	MSI2
4165193_106	GL019119	147,630	4.901e-07	0.001051	0.008411
4223149_99	GL019332	5,745	6.122e-07	0.001167	0.01051
1089925_112	7	35,584,375	9.805e-07	0.001583	0.01683
464226_128	2	125,603,458	1.014e-06	0.001583	0.01741	BCL11A
3374849_109	GL018704	1,294,009	1.387e-06	0.001984	0.02381
3695415_108	GL018751	737,318	1.837e-06	0.002395	0.03153	TRAF3
3695415_108	GL018751	737,318	1.837e-06	0.002395	0.03153	RCOR1
2099634_108	13	45,086,624	2.028e-06	0.002395	0.0348	WARS2
2099634_108	13	45,086,624	2.028e-06	0.002395	0.0348	TBX15
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593	DPPA5
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593
1902686_98	12	60,113,885	2.094e-06	0.002395	0.03593

Figure 3a: Manhattan plot showing results for all SNPs on chromosomes 1:21 for analysis by genotype. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs with an adjusted p-value < 0,05 shown in green

Variants detected as significant to an FDR of 5%. Those denoted with an asterisk received a Bonferroni-adjusted p-value < 0.05 and these typically involved reduction of the minor allele frequency and a shift towards homozygous reference. The remaining sites showed a combination of the same and increasing minor allele abundance, with some variants becoming exclusively heterozygous in the 2012 population.

Allele Frequency Model

This model tests:
H₀: No association between allele frequencies and populations
H_A: An association exists between allele frequencies and populations

alleleResults <- genotypes %>%
    filter(Population != 2010,
           !variant.id %in% regionSNPs$variant.id) %>%
    group_by(snpID, Population) %>%
    summarise(P = sum(2 - Genotype),
              Q = sum(Genotype)) %>%
    ungroup() %>%
    split(f = .$snpID) %>%
    mclapply(function(x){
        m <- as.matrix(x[c("P", "Q")])
        ft <- list(p.value = NA) 
        if (length(m) == 4) {
            ft <- fisher.test(m)
        }
        x %>%
            mutate(MAF = Q / (P + Q)) %>%
            dplyr::select(snpID, Population, MAF) %>%
            spread(Population, MAF) %>%
            mutate(p = ft$p.value)
    },mc.cores = mc) %>%
    bind_rows() %>%
    filter(!is.na(p)) %>%
    dplyr::rename(MAF_1996 = `1996`,
                  MAF_2012 = `2012`) %>%
    mutate(FDR = p.adjust(p, "fdr"),
           adjP = p.adjust(p, "bonferroni")) %>%
    arrange(p) %>%
    left_join(genotypes %>%
                  distinct(snpID, chromosome, position)) %>%
    dplyr::select(snpID, chromosome, position, everything())

Under this model:

• 5 SNP alleles were detected as being significantly associated with the two populations when controlling the FWER at α = 0.05. However, as these SNPs were within 21nt of each other, this may represent the same haplotype
• 38 SNP alleles were detected as being significantly associated with the two populations when controlling the FDR at α = 0.05

SNPs considered as significant when analysing by genotype using an FDR cutoff of 0.05

snpID	chromosome	position	MAF_1996	MAF_2012	p	FDR	adjP	Gene within 100kb
3391201_92	GL018705	1,937,359	0.2255	0.009615	3.44e-07	0.005911	0.005916
916731_91	4	89,602,973	0.1961	0	6.875e-07	0.005911	0.01182	RIC8B
916731_91	4	89,602,973	0.1961	0	6.875e-07	0.005911	0.01182	RFX4
3040789_114	19	30,689,426	0.1863	0	1.531e-06	0.007628	0.02632	MSI2
321186_117	2	2.8e+07	0.25	0.0122	1.866e-06	0.007628	0.03209	KLF3
1009106_1	6	9,037,073	0.4432	0.1277	2.52e-06	0.007628	0.04333	SMG1
1009106_1	6	9,037,073	0.4432	0.1277	2.52e-06	0.007628	0.04333	ARL6IP1
836950_151	4	35,111,855	0.2907	0.04082	3.086e-06	0.007628	0.05307	TMPRSS12
836950_151	4	35,111,855	0.2907	0.04082	3.086e-06	0.007628	0.05307	METTL7A
836950_151	4	35,111,855	0.2907	0.04082	3.086e-06	0.007628	0.05307	SLC11A2
464226_128	2	125,603,458	0.1915	0	3.105e-06	0.007628	0.0534	BCL11A
2227006_109	13	129,650,867	0.2708	0.03333	4.172e-06	0.008366	0.07174	ID3
2227006_109	13	129,650,867	0.2708	0.03333	4.172e-06	0.008366	0.07174	E2F2
2227006_109	13	129,650,867	0.2708	0.03333	4.172e-06	0.008366	0.07174	ASAP3
2227006_109	13	129,650,867	0.2708	0.03333	4.172e-06	0.008366	0.07174	TCEA3
2227006_109	13	129,650,867	0.2708	0.03333	4.172e-06	0.008366	0.07174
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166	DPPA5
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
1902686_98	12	60,113,885	0.1633	0	4.749e-06	0.008366	0.08166
3374849_109	GL018704	1,294,009	0.2065	0.0102	4.865e-06	0.008366	0.08366
4165193_106	GL019119	147,630	0.2736	0.04167	5.576e-06	0.008717	0.09589
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	FNBP1
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	USP20
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	C9orf78
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	TOR1A
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	TOR1B
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	PTGES
4044777_119	GL018933	204,058	0.01754	0.2019	7.749e-06	0.0111	0.1333	PRRX2
1089925_112	7	35,584,375	0.2841	0.04348	9.725e-06	0.01286	0.1672
3695415_108	GL018751	737,318	0.2609	0.03261	1.275e-05	0.01447	0.2193	TRAF3
3695415_108	GL018751	737,318	0.2609	0.03261	1.275e-05	0.01447	0.2193	RCOR1
4223149_99	GL019332	5,745	0.3077	0.05814	1.314e-05	0.01447	0.226
2099634_108	13	45,086,624	0.2609	0.03409	1.412e-05	0.01447	0.2429	WARS2
2099634_108	13	45,086,624	0.2609	0.03409	1.412e-05	0.01447	0.2429	TBX15
87917_119	1	64,635,286	0.186	0	1.43e-05	0.01447	0.2459	CEP78
87917_119	1	64,635,286	0.186	0	1.43e-05	0.01447	0.2459	PSAT1
4010189_98	GL018907	283,766	0.1932	0.01	1.627e-05	0.01495	0.2798	BRWD1
4010189_98	GL018907	283,766	0.1932	0.01	1.627e-05	0.01495	0.2798	LCA5L
4010189_98	GL018907	283,766	0.1932	0.01	1.627e-05	0.01495	0.2798
2689075_49	16	56,527,308	0.05814	0.3043	1.652e-05	0.01495	0.284	ESRRG
4149102_107	GL019077	34,265	0.2232	0.02941	2.279e-05	0.01959	0.3919	IFT140
4149102_107	GL019077	34,265	0.2232	0.02941	2.279e-05	0.01959	0.3919	TMEM204
4149102_107	GL019077	34,265	0.2232	0.02941	2.279e-05	0.01959	0.3919	CRAMP1
4149102_107	GL019077	34,265	0.2232	0.02941	2.279e-05	0.01959	0.3919
1997286_93	12	141,844,290	0.08889	0.3556	2.435e-05	0.01987	0.4187	ESR1
2906428_79	18	25,311,176	0.1731	0.4434	2.542e-05	0.01987	0.4371	CDK1
2405359_66	14	97,813,263	0.02083	0.2021	4.48e-05	0.0335	0.7704	STXBP5L
3000704_65	19	13,055,154	0.03191	0.234	5.416e-05	0.03776	0.9314
4146664_90	GL019084	74,038	0.1961	0.02041	5.49e-05	0.03776	0.9441	GNB1
4146664_90	GL019084	74,038	0.1961	0.02041	5.49e-05	0.03776	0.9441	NADK
4146664_90	GL019084	74,038	0.1961	0.02041	5.49e-05	0.03776	0.9441
4098854_101	GL018985	129,495	0.1702	0.01042	6.686e-05	0.04259	1
4098854_101	GL018985	129,495	0.1702	0.01042	6.686e-05	0.04259	1	UFD1
4098854_101	GL018985	129,495	0.1702	0.01042	6.686e-05	0.04259	1	C22orf39
4098854_101	GL018985	129,495	0.1702	0.01042	6.686e-05	0.04259	1	MRPL40
4098854_101	GL018985	129,495	0.1702	0.01042	6.686e-05	0.04259	1	HIRA
4098838_14	GL018985	123,328	0.3636	0.1275	6.797e-05	0.04259	1
4098838_14	GL018985	123,328	0.3636	0.1275	6.797e-05	0.04259	1	UFD1
4098838_14	GL018985	123,328	0.3636	0.1275	6.797e-05	0.04259	1	C22orf39
4098838_14	GL018985	123,328	0.3636	0.1275	6.797e-05	0.04259	1	MRPL40
4098838_14	GL018985	123,328	0.3636	0.1275	6.797e-05	0.04259	1	HIRA
1882774_17	12	40,509,779	0.3214	0.5943	7.288e-05	0.04259	1
1045185_75	7	2,702,919	0.2143	0.03774	7.807e-05	0.04259	1
1045185_75	7	2,702,919	0.2143	0.03774	7.807e-05	0.04259	1	ZNF212
1045185_75	7	2,702,919	0.2143	0.03774	7.807e-05	0.04259	1	ZNF282
1045185_75	7	2,702,919	0.2143	0.03774	7.807e-05	0.04259	1	ZNF398
3016258_99	19	19,178,148	0.22	0.03261	7.861e-05	0.04259	1	SEZ6
3016258_99	19	19,178,148	0.22	0.03261	7.861e-05	0.04259	1	PHF12
3016258_99	19	19,178,148	0.22	0.03261	7.861e-05	0.04259	1	DHRS13
3016258_99	19	19,178,148	0.22	0.03261	7.861e-05	0.04259	1	FLOT2
3016258_99	19	19,178,148	0.22	0.03261	7.861e-05	0.04259	1	ERAL1
3016258_99	19	19,178,148	0.22	0.03261	7.861e-05	0.04259	1	FAM222B
2028473_73	13	2,979,383	0.01818	0.1731	8.051e-05	0.04259	1	COP1
3829050_109	GL018791	957,960	0.163	0.009615	8.094e-05	0.04259	1	FHAD1
3829050_109	GL018791	957,960	0.163	0.009615	8.094e-05	0.04259	1	TMEM51
2580149_42	15	87,473,844	0.3511	0.1154	8.416e-05	0.04259	1	ADGRL3
2527760_104	15	43,156,117	0.1531	0.4057	8.594e-05	0.04259	1	GIMD1
2527760_104	15	43,156,117	0.1531	0.4057	8.594e-05	0.04259	1	AIMP1
2527760_104	15	43,156,117	0.1531	0.4057	8.594e-05	0.04259	1	TBCK
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1	SERPINA6
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1	SERPINA11
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1	SERPINA9
3999128_75	GL018883	283,912	0.3654	0.125	8.683e-05	0.04259	1	SERPINA12
4176850_23	GL019154	883	0.234	0.04082	8.916e-05	0.04259	1	MTHFSD
4176850_23	GL019154	883	0.234	0.04082	8.916e-05	0.04259	1	FOXF1
686773_29	3	90,851,512	0.4375	0.1667	9.731e-05	0.04522	1	CRISPLD1
686773_29	3	90,851,512	0.4375	0.1667	9.731e-05	0.04522	1
3032107_92	19	26,020,372	0.1939	0.02083	0.0001019	0.04612	1

## png 
##   2

Manhattan plot showing results for all SNPs on chromosomes 1:21 when analysing by allele frequencies. The horizontal line indicates the cutoff for an FDR of 5%, with SNPs considered significant under the Bonferroni adjustment shown in green.

Comparison of minor allele frequencies in both populations. SNPs with an FDR-adjusted p-value < 0.05 are coloured red, whilst those with a Bonferroni-adjusted p-value are additionally labelled.

gdsFile <- seqOpen(gdsPath, readonly = TRUE)
seqSetFilter(gdsFile, 
             variant.id = genotypes %>% 
                 distinct(variant.id, snpID) %>%
                 filter(snpID %in% filter(regionResults, p > 0.05)$snpID) %>% 
                 .[["variant.id"]],
             sample.id = sampleID %>% 
                 filter(Population %in% c(1996, 2012)) %>% 
                 .[["Sample"]])
seqGDS2VCF(gdsFile, "../5_stacks/vcf/filtered.vcf.gz")
seqResetFilter(gdsFile)
seqClose(gdsFile)

Session Information

R version 3.6.0 (2019-04-26)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_AU.UTF-8, LC_NUMERIC=C, LC_TIME=en_AU.UTF-8, LC_COLLATE=en_AU.UTF-8, LC_MONETARY=en_AU.UTF-8, LC_MESSAGES=en_AU.UTF-8, LC_PAPER=en_AU.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_AU.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats4, parallel, grid, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: rtracklayer(v.1.44.0), plyranges(v.1.4.1), GenomicRanges(v.1.36.0), GenomeInfoDb(v.1.20.0), IRanges(v.2.18.1), S4Vectors(v.0.22.0), BiocGenerics(v.0.30.0), ggrepel(v.0.8.1), qqman(v.0.1.4), ggsn(v.0.5.0), rgdal(v.1.4-4), ggmap(v.3.0.0), sp(v.1.3-1), readxl(v.1.3.1), forcats(v.0.4.0), stringr(v.1.4.0), dplyr(v.0.8.1), purrr(v.0.3.2), readr(v.1.3.1), tidyr(v.0.8.3), tibble(v.2.1.3), ggplot2(v.3.1.1), tidyverse(v.1.2.1), magrittr(v.1.5), scales(v.1.0.0), pander(v.0.6.3), SNPRelate(v.1.18.0), SeqArray(v.1.24.0) and gdsfmt(v.1.20.0)

loaded via a namespace (and not attached): nlme(v.3.1-140), matrixStats(v.0.54.0), bitops(v.1.0-6), sf(v.0.7-4), lubridate(v.1.7.4), httr(v.1.4.0), tools(v.3.6.0), backports(v.1.1.4), R6(v.2.4.0), KernSmooth(v.2.23-15), DBI(v.1.0.0), lazyeval(v.0.2.2), colorspace(v.1.4-1), withr(v.2.1.2), tidyselect(v.0.2.5), curl(v.3.3), compiler(v.3.6.0), Biobase(v.2.44.0), cli(v.1.1.0), rvest(v.0.3.4), xml2(v.1.2.0), DelayedArray(v.0.10.0), labeling(v.0.3), classInt(v.0.3-3), Rsamtools(v.2.0.0), digest(v.0.6.19), foreign(v.0.8-71), rmarkdown(v.1.13), XVector(v.0.24.0), jpeg(v.0.1-8), pkgconfig(v.2.0.2), htmltools(v.0.3.6), highr(v.0.8), rlang(v.0.3.4), rstudioapi(v.0.10), generics(v.0.0.2), jsonlite(v.1.6), BiocParallel(v.1.18.0), RCurl(v.1.95-4.12), GenomeInfoDbData(v.1.2.1), Matrix(v.1.2-17), Rcpp(v.1.0.1), munsell(v.0.5.0), stringi(v.1.4.3), yaml(v.2.2.0), SummarizedExperiment(v.1.14.0), zlibbioc(v.1.30.0), plyr(v.1.8.4), maptools(v.0.9-5), crayon(v.1.3.4), lattice(v.0.20-38), Biostrings(v.2.52.0), haven(v.2.1.0), hms(v.0.4.2), knitr(v.1.23), pillar(v.1.4.1), rjson(v.0.2.20), XML(v.3.98-1.20), glue(v.1.3.1), evaluate(v.0.14), calibrate(v.1.7.2), modelr(v.0.1.4), png(v.0.1-7), RgoogleMaps(v.1.4.3), cellranger(v.1.1.0), gtable(v.0.3.0), assertthat(v.0.2.1), xfun(v.0.7), broom(v.0.5.2), e1071(v.1.7-2), class(v.7.3-15), GenomicAlignments(v.1.20.0) and units(v.0.6-3)